TILDE: TILt-based Distributional Erasure for Concept Unlearning
Concept unlearning in text-to-image diffusion models is critical for safe and practical deployment: with rising privacy concerns, copyright disputes, trademark constraints, and safety regulations, deployed systems must be able to suppress unwanted concepts after training. Existing methods often remove the target concept effectively, but practical unlearning also requires an equally fundamental property: the unlearned model should retain quality, diversity, and semantic coverage on benign generat
Record details
Published: 7 July 2026
Source: arXiv
Category: Research
Topics: Regulation · Privacy · Copyright & IP
Retrieved: 14 July 2026
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ethics.ai (7 July 2026), “TILDE: TILt-based Distributional Erasure for Concept Unlearning,” evidence record 151, https://ethics.ai/record/151 (originally published by arXiv).
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